mcp-google-health

create_data_point

Write a new Google Health data point, such as weight, sleep, hydration, mood, symptoms, or menstrual period, when you have the matching write scope.

Instructions

Write a new data point, such as a weight, body fat, height, sleep, exercise, hydration or nutrition log, mood, symptom, menstrual period, or ovulation test. Needs the matching write scope.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dataType": "<dataType>",
      "dataPoint": "<dataPoint>"
    },
    "name": "create_data_point"
  }
}

Input Schema

Name	Required	Description
`dataType`	Yes	Data type in kebab case, such as steps, sleep, or heart-rate. See list_data_types.
`dataPoint`	Yes	DataPoint body. Put the payload under the camelCase data type field, such as {"weight": {...}}. Call describe_schema with the data type for its fields.
`apiVersion`	No	API version. Defaults to v4, or v4beta for beta-only data types.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 10, 2026

TDQS

B3.4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=false, destructiveHint=false, and openWorldHint=true. The description adds an authorization requirement (matching write scope), which is real context beyond the annotations. It stops short of disclosing what happens on duplicate or conflicting data points, or what the call returns, which matters for a write tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with the verb and resource front-loaded and the prerequisite following. The long example list is informative rather than padded, though it is the bulkiest part of an otherwise tight definition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a write tool with a nested free-form payload object and no output schema, the description covers purpose and auth but says nothing about the response or how conflicts are handled. The schema carries the payload guidance, so the gap is moderate rather than critical.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the schema already points to list_data_types and describe_schema for the dataType and dataPoint payload. The description's enumeration of data types adds flavor but no syntax or format detail beyond the schema, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb and resource ('Write a new data point') and enumerates concrete examples (weight, sleep, mood, ovulation test), which makes the scope of the resource tangible. It does not explicitly contrast itself with update_data_point or reconcile_data_points, but 'new' signals creation clearly enough.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It states a prerequisite ('Needs the matching write scope'), which is genuinely useful before calling. However, there is no explicit when-to-use framing against siblings such as update_data_point or a roll-up tool, so the agent must infer that this is for brand-new records rather than modifications.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.